From idea to impact: 5 real-world AI use cases in Dynamics 365

Many companies are already experimenting with AI, but few move beyond pilots. Experiments often stay stuck in isolated tools, proof-of-concepts, or one-off use cases.

At 9altitudes, we help organizations take the next step: turning AI into scalable systems that deliver measurable impact. Depending on the challenge, we use Copilot Studio or custom AI models inside Dynamics 365, giving companies the right tool to capture real business value.

In this article, we’ll show five practical use cases where AI is already reshaping sales, service, and operations.

The challenge:
Sales teams build years of customer knowledge through email conversations, but much of that data never makes it into CRM. As a result, valuable history is locked away in individual mailboxes, invisible to the rest of the organization.

The solution:
9altitudes developed an AI agent that scans historical emails (with the salesperson’s consent) and automatically extracts relevant customer interactions, contact details, and insights. The data is presented in a structured overview, ready to enrich Dynamics 365.

The impact:
Salespeople no longer need to search through years of emails. Instead, they quickly get a complete view of customer history. This makes CRM data more accurate, helps new team members get up to speed faster, and builds stronger customer relationships. By bringing old conversations back into the system, companies create a more solid base for forecasting and account management.

2. Smarter Field Service: Goodbye to Handwritten Forms

The challenge:
Field technicians at one industrial company still rely on handwritten paper forms to document work, materials, and observations. Back-office staff then spend countless hours deciphering handwriting and re-entering data into ERP.

The solution:
A field service agent will digitize this workflow. Technicians will be able to either dictate their notes (speech-to-text) or take photos of handwritten forms. The AI model will recognize handwriting, extract materials used and actions taken, and produce a clean digital record for ERP.

The impact:
This eliminates repetitive data entry and reduces costly errors. It also accelerates invoicing and provides customers with more accurate documentation. Over time, digitized service data could even feed into predictive analytics, helping companies move from reactive to proactive maintenance.

3. Automated Risk Evaluation in Sales Orders

The challenge:
Evaluating the financial risk of new sales orders typically requires manual checks in ERP and finance systems. This is a time-consuming task that often happens too late.

The solution:
Using Copilot Studio, 9altitudes designed an agent that monitors incoming sales orders in Outlook, cross-checks them with ERP data in Dynamics 365 Finance & Operations, and automatically flags potential risks. For example, if a customer has a history of late payments, the agent recommends adjusted payment terms.

The impact:
Sales teams can quickly see the financial risk of a customer. This helps them make smarter choices before closing a deal. It protects profit margins and builds trust with finance, because both teams work with the same information in real time.

4. Objective and Scalable Inspection Reports

The challenge:
In safety and quality inspections, employees typically record observations in free text, such as “workers are wearing helmets, but not secured.” Reviewing and categorizing these reports manually is labor-intensive and often subjective.

The solution:
9altitudes built a custom AI model that automatically analyzes and classifies inspection texts. Using a hybrid approach that combines rule-based logic with machine learning – where the rules provide essential context to the ML model – the system identifies categories (e.g., safety, work environment) and sentiment (positive or negative).

The impact:
Inspections become faster, more consistent, and less dependent on human interpretation. Managers gain objective insights across hundreds of reports, helping them spot recurring safety risks early. The approach also scales easily: what once took hours of manual review can now be automated across thousands of inspections.

5. Predictive Maintenance Across Multiple Plants

The challenge:
Manufacturers often struggle with fragmented data structures across different plants, making predictive maintenance nearly impossible at scale. Each site may collect sensor data differently, leading to inconsistent results.

The solution:
By standardizing data into one structure and training a predictive AI model, 9altitudes helped a company monitor machine health across multiple factories. The model focuses on key indicators such as temperature and vibration to forecast future failures.

The impact:
Instead of unplanned downtime and reactive repairs, plants can schedule maintenance proactively. Today, every factory often collects sensor data in a different way. By first creating one shared data structure, it becomes possible to compare machines across plants and train one model that works for all identical machines. That way, insights from one site can be reused in another, and new models can be trained more easily thanks to the wealth of accumulated information. Over time, this reduces downtime, extends machine lifecycles, and lowers costs.